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財務危機模型的研究一般納入財務正常公司與財務危機公司兩者當樣本，探討區分危機與正常公司的因素，本研究則進一步以財務危機公司為樣本，探討在財務危機公司中區分舞弊公司與正常經營公司的基本因素。 本研究從財務危機公司中，分出財務舞弊公司與正常經營公司，因此研究樣本包含發生舞弊的財務危機公司與正常經營而發生財務危機的公司。研究變數則從文獻篩選23個財務解釋變數，以及13個公司治理解釋變數，運用羅吉斯迴歸法進行實證，結果顯示3個財務變數和1個公司治理變數在區分財務危機公司中的財務舞弊公司與正常經營公司有顯著的區別能力，公司治理變數的董監事持股比率尤其顯著。 / Financial distress prediction is usually based on both financial distressed firms and non-distressed firms. Based on financial distressed firms, this study further investigates the factors distinguishing financial fraud firms from non-fraud firms. The sample includes fraud and no-fraud firms while both are financial distressed. Twenty-three financial and thirteen corporate governance variables are surveyed from literature. The empirical result of logit regression shows that three financial variables and one corporate governance variable are significant factors in distinguishing fraud from no-fraud firms in distressed companies. Especially, the percentage of holding stocks of board of directors is the most significant variable.

1999年新巴塞爾協定規定鼓勵銀行採用內部信用評等法(internal ratings based approach)，以衡量貸款者無法償還之風險以計提最低資本。為因應此一授信風險控管之需要，銀行亟需建立一套有效之財務危機預警系統，以判定銀行授信客戶發生財務危機之機率。 本研究運用羅吉斯迴歸分析(logistic regression analysis)與離散時間涉險分析(discrete-time survival analysis)分法於三種相互具有巢狀式關連性之財務危機預測模型，逐步加入財務、非財務及公司治理變數，以便在同一種分析方法下比較三種模型，以及在同一種模型下比較兩種分析方法。實證結果顯示，就樣本期間內而言，同一種分析方法下模型之財務危機預測能力，隨著不同種類解釋變數之加入而逐步提高。然而，就樣本期間外而言，同一種分析方法下模型之財務危機預測能力，並未隨著不同種類解釋變數之加入而逐步提高，但分類能力皆十分優良；而在同一種模型下離散時間涉險分析方法之整體分類能力皆高於羅吉斯迴歸分析方法。 / The 1999 Basel II Accord suggests banks measure the impossibility of reimbursement of debtors to calculate capital minimums by internal ratings-based approach. To reduce the credit risk, it is important that banks construct accurate financial distress prediction systems to determine the probability of financial distress of debtors. This study employs logistic regression and discrete-time hazard analysis to construct nested models to which the financial, non-financial, and corporate governance corporate variables are added step by step. I therefore make comparison of the performance of three models under logistic regression and discrete-time hazard analysis, respectively. Meanwhile, the comparison of the performance of logistic regression and discrete-time hazard analyses under each of three models is also made. The empirical results show that the in-sample predictive ability of financial distress is enhanced by gradually incorporating different kinds of variables in both analyses. Although the out-of-the-sample predictive ability of financial distress is not improved by gradually incorporating different kinds of variables in one analysis, the model performance is quite well overall. The entire discriminability of discrete-time hazard analysis is better than logistic regression under each model.

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.

Nombreuses sont les villes françaises à abriter des éléments d’architecture militaire (citadelles, casernes, bastions,murailles, etc.). Bien que les fonctions défensives à l’origine de ces ouvrages soient peu à peu tombées en désuétude,ceux-ci sont ancrés dans les tissus urbains des cités qui les abritent aujourd’hui encore. Ils participent des morphologiesurbaines, des schémas fonctionnels et, plus globalement, de la manière dont la notion de ville est appréhendée.Les villes font dorénavant face à de nouveaux enjeux qui poussent à renouveler les cadres au travers desquelles ellessont traditionnellement approchées. L’étalement urbain, ses conséquences, la nécessité de tendre vers un urbanismerenouvelé et plus durable amènent à porter un nouveau regard sur les fortifications urbaines. Au-delà de son aspectsymbolique, ce type d’architecture est marqué par des caractéristiques spatiales hors-normes (localisation, volumétrie,géométrie) qui engagent à l’analyser au travers des prismes méthodologiques et conceptuels que fournit la géographie.Comment l’héritage des fortifications urbaines peut-il aujourd’hui être mobilisé afin de créer un espace urbain cohérent,tant sur le plan spatial que social ou symbolique et ainsi répondre aux aspiration des villes actuelles ?La première partie de la thèse porte sur les enjeux auxquels les “villes fortes” sont aujourd’hui confrontées (modernisation,croissance étalement urbain, etc.) et la manière dont leurs “ouvrages lourds” sont affectés. La deuxième partiedu travail s’attache à caractériser le phénomène des “villes fortes” en France aujourd’hui par le biais d’approchesdescriptives, empiriques et statistiques qui permettent de formaliser et d’analyser les logiques et les règles qui régissentle devenir des anciens ouvrages défensifs. La dernière partie de la thèse étudie le potentiel d’urbanisation des zones etouvrages militaires et propose de mesurer l’impact de leur transformation en logements dans le cadre d’un scénariode renouvellement urbain compact pour la ville de Besançon. Il s’agit d’évaluer les conséquences de telles mesuressur le schéma fonctionnel des villes et les formes urbaines. L’ensemble de ces travaux montre l’intérêt du recours auxouvrages défensifs dans le cadre d’un urbanisme à la fois renouvelé, mais aussi plus “durable”, en conformité avec lesformes urbaines, les demandes contemporaines et les projets des villes. / A large number of french cities host military historical edifices (citadels, barracks, bastions, defensive walls, etc.). Although their initial defensive functions have been lost over time, these edifices remain deeply rooted in the urban fabric of their host cities. They continue exerting an impact on these cities’ urban morphology and modern-time functions as well as the way in which the concept of city is understood. Cities nowadays face some new challenges,the increasing awareness of urban sprawl and its consequences, coupled with an urge to promote a renewed and sustainable urbanism, invites us to adopt new approaches to study urban fortifications. In addition to their symbolic aspect, fortifications are characterized by their out-of-the-common spatial measure (location, volume and geometry),requiring researchers to use methodologies and geographical concepts for their academic endeavour. Against this background : how can urban fortifications be used to create urban space that is spatially, socially and symbolically coherent with their historical heritage and that also satisfies the functional need of modern cities ?The first part of this doctoral dissertation present modern-day challenges that fortified cities face (modernisation,urban expansion and sprawl, etc.), and the ways in which their “heavy buildings” are affected. The second part sheds light on the relationships between cities and their fortifications, thanks to descriptive, empirical and statistical analyses. The statistical study noticeably builds on a database to examine the rationale and rules underpinning there habilitation of ancient military edifices. The last part of the dissertation proposes an evaluation of the potential of rehabilitating ancient military edifices and spaces for modern urban usage. For this purpose the impact of transforming these fortifications into residential blocks is assessed in a fortified French city (Besançon). This assessment aims at evaluating the consequences of such urban planning measures on urban morphology and functions. Overall, the analysis provided in that doctoral dissertation demonstrates that ancient military buildings have their place in contemporary urban planning. They help to achieve a renewed and sustainable urban design in line with the urban morphology,modern social and functional requirements and the development perspectives in their host cities.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.